Udemy course I completed in the Summer of 2018. Covers how to make use of:
REGRESSION:
- Simple Linear Regression
- Multiple Linear Regression
- Polynomial Regression
- Support Vector Regression
- Decision Tree Regression
- Random Forest Regression
CLASSIFICATION:
- Logistic Regression
- K-Nearest Neighbor
- Support Vector Machines
- Kernel SVM
- Naive Bayes
- Decision Tree
- Random Forest
CLUSTERING:
- K-Means
- Hierarchical
ASSOCIATION RULE LEARNING
- Apriori
- Eclat
REINFORCEMENT LEARNING
- Upper Confidence Bound
- Thompson Sampling
NATURAL LANGUAGE PROCESSING
- Bag of words model
DEEP LEARNING
- Artificial Neural Networks
- Convolutional Neural Networks
DIMENSIONALITY REDUCTION
- Principal Component Analysis
- Linear Discrimant Analysis
- Kernel PCA